Build an Article Recommendation Engine Using Machine Learning
Python Flask app uses Pinecone — a similarity search service — to create our own article recommendation engine. The app uses a dataset of 143,000 news articles from Kaggle. It uses similarity search and machine learning algorithms to determine similarities between various inputs. When users submit their reading history, a request is made to an API endpoint that uses the Pinecone SDK to query the index of vector embeddings. The endpoint returns 10 similar news articles and displays them in the app’s UI. There are 1,024 possible combinations of reading history that can be used as input in this example.
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